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A Wireless Charging Facilities Deployment Problem Considering Optimal Traffic Delay and Energy Consumption on Signalized Arterial

机译:考虑信号干线最佳交通时延和能耗的无线充电设施部署问题

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With the looming promise of wireless recharging technology, electric vehicles (EVs) are going to be able to acquire energy while still in motion. This paper focuses on the optimal deployment of wireless recharging facilities on signalized arterials for EVs. To address this issue, a bi-objective model considering both traffic operation efficiency (i.e., traffic delay saving) and charging infrastructure utilization rate (i.e., electricity gain from charging) has been formulated. A modified cell transmission model (CTM) is used as a base to simulate traffic flow on an arterial with traffic signals. The cells in the CTM also serve as a potential installation site for wireless recharging facilities. The essential goal of this model is to maximize the recharging electricity for EVs traveling on arterials while maintaining low travel delay. Due to the complexity in solving the bi-objective model, heuristic approaches, such as genetic algorithm and particle swarm optimization, are employed. The numerical experiments based on real day-to-day traffic demand are executed. A Pareto set is obtained and a sensitivity analysis regarding recharging rate, investment, and minimum recharging region length is provided.
机译:随着无线充电技术的迫在眉睫的前景,电动汽车(EV)将能够在行驶中获得能量。本文着眼于在电动汽车的信号干线上无线充电设施的最佳部署。为了解决该问题,已经建立了既考虑交通运营效率(即节省交通延迟)又考虑了充电基础设施利用率(即充电产生的电力收益)的双目标模型。修改后的信元传输模型(CTM)被用作模拟交通信号在动脉上流动的基础。 CTM中的电池还可以作为无线充电设施的潜在安装场所。该模型的基本目标是在保持较低的行驶延迟的同时,使通过动脉行驶的电动汽车的充电电量最大化。由于求解双目标模型的复杂性,因此采用了启发式方法,例如遗传算法和粒子群优化。进行了基于实际日常交通需求的数值实验。获得帕累托集合,并提供关于充电率,投资和最小充电区域长度的敏感性分析。

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